This study deals with the analysis of regional economic development in Europe. Specifically, it examines the extent to which the performance and characteristics of higher education systems (HESs) influence regional economic development. The analysis employs data at the regional level, examining 649 NUTS-3 in 29 European countries, from 2014 to 2016. The empirical analysis, based on an original dataset that we developed, employs a novel methodological strategy that combines a traditional econometric approach with random forest. The findings detect the existence of nonlinear relationships between regional GDP per capita and HES indicators, which could have been overlooked by previous studies in the literature. Furthermore, the empirical results demonstrate the importance of comprehensively modelling the diversity of HESs, since distinct characteristics and performance can contribute differently to the economy of the regions. In particular, the most important factors for regional economic development are the size of HESs, the internationalisation of the students and research productivity. Finally, this paper provides useful insights for policymakers by suggesting new instruments for driving and fostering the economic development of their regions.

Higher education systems and regional economic development in Europe: A combined approach using econometric and machine learning methods

Bertoletti A.;Agasisti T.
2022

Abstract

This study deals with the analysis of regional economic development in Europe. Specifically, it examines the extent to which the performance and characteristics of higher education systems (HESs) influence regional economic development. The analysis employs data at the regional level, examining 649 NUTS-3 in 29 European countries, from 2014 to 2016. The empirical analysis, based on an original dataset that we developed, employs a novel methodological strategy that combines a traditional econometric approach with random forest. The findings detect the existence of nonlinear relationships between regional GDP per capita and HES indicators, which could have been overlooked by previous studies in the literature. Furthermore, the empirical results demonstrate the importance of comprehensively modelling the diversity of HESs, since distinct characteristics and performance can contribute differently to the economy of the regions. In particular, the most important factors for regional economic development are the size of HESs, the internationalisation of the students and research productivity. Finally, this paper provides useful insights for policymakers by suggesting new instruments for driving and fostering the economic development of their regions.
Economic development
Higher education
Machine learning
Regions
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1209934
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